code: def create_rename_keys(config):
      rename_keys = []
      # fmt: off
  
      # patch embedding layer
      rename_keys.append(("cls_token", "embeddings.cls_token"))
      rename_keys.append(("mask_token", "embeddings.mask_token"))
      rename_keys.append(("pos_embed", "embeddings.position_embeddings"))
      rename_keys.append(("patch_embed.proj.weight", "embeddings.patch_embeddings.projection.weight"))
      rename_keys.append(("patch_embed.proj.bias", "embeddings.patch_embeddings.projection.bias"))
  
      for i in range(config.num_hidden_layers):
          # layernorms
          rename_keys.append((f"blocks.{i}.norm1.weight", f"encoder.layer.{i}.norm1.weight"))
          rename_keys.append((f"blocks.{i}.norm1.bias", f"encoder.layer.{i}.norm1.bias"))
          rename_keys.append((f"blocks.{i}.norm2.weight", f"encoder.layer.{i}.norm2.weight"))
          rename_keys.append((f"blocks.{i}.norm2.bias", f"encoder.layer.{i}.norm2.bias"))
          # MLP
          if config.use_swiglu_ffn:
              rename_keys.append((f"blocks.{i}.mlp.w12.weight", f"encoder.layer.{i}.mlp.w12.weight"))
              rename_keys.append((f"blocks.{i}.mlp.w12.bias", f"encoder.layer.{i}.mlp.w12.bias"))
              rename_keys.append((f"blocks.{i}.mlp.w3.weight", f"encoder.layer.{i}.mlp.w3.weight"))
              rename_keys.append((f"blocks.{i}.mlp.w3.bias", f"encoder.layer.{i}.mlp.w3.bias"))
          else:
              rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"encoder.layer.{i}.mlp.fc1.weight"))
              rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"encoder.layer.{i}.mlp.fc1.bias"))
              rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"encoder.layer.{i}.mlp.fc2.weight"))
              rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"encoder.layer.{i}.mlp.fc2.bias"))
          # layerscale
          rename_keys.append((f"blocks.{i}.ls1.gamma", f"encoder.layer.{i}.layer_scale1.lambda1"))
          rename_keys.append((f"blocks.{i}.ls2.gamma", f"encoder.layer.{i}.layer_scale2.lambda1"))
          # attention projection layer
          rename_keys.append((f"blocks.{i}.attn.proj.weight", f"encoder.layer.{i}.attention.output.dense.weight"))
          rename_keys.append((f"blocks.{i}.attn.proj.bias", f"encoder.layer.{i}.attention.output.dense.bias"))
  
      # final layernorm
      rename_keys.append(("norm.weight", "layernorm.weight"))
      rename_keys.append(("norm.bias", "layernorm.bias"))
  
      # fmt: on
      return rename_keys
  
file_path: transformers/src/transformers/models/dinov2/convert_dinov2_to_hf.py
imports: import argparse
  import json
  from pathlib import Path
  import requests
  import torch
  import torch.nn
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from torchvision import transforms
  from transformers import BitImageProcessor, Dinov2Config, Dinov2ForImageClassification, Dinov2Model
  from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
  from transformers.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: create_rename_keys(config) and returns: rename_keys